{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Using LDA for classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conda install -c anaconda pandas-datareader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pandas_datareader import data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xa53aa58>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WegaHk+Qnehrw8uXL276ys7N7errfrF69mgULFhAREUF8fDwLFy7kD3/4AwDXXXcdBoOB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      "text/plain": [
       "<matplotlib.figure.Figure at 0xa52e7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from pandas_datareader import data\n",
    "import pandas as pd\n",
    "\n",
    "tickers = [\"F\", \"TM\", \"GM\", \"TSLA\"]\n",
    "\n",
    "first_date = '2009-01-01'\n",
    "last_date = '2016-12-31'\n",
    "\n",
    "\n",
    "stock_panel = data.DataReader(tickers, 'google', first_date, last_date)\n",
    "\n",
    "stock_df = stock_panel.Close.dropna()\n",
    "stock_df.plot(figsize=(12, 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>is_higher</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th>minor</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">2016-10-10</th>\n",
       "      <th>F</th>\n",
       "      <td>12.35</td>\n",
       "      <td>12.38</td>\n",
       "      <td>12.10</td>\n",
       "      <td>12.12</td>\n",
       "      <td>24951437.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GM</th>\n",
       "      <td>32.31</td>\n",
       "      <td>32.60</td>\n",
       "      <td>32.12</td>\n",
       "      <td>32.15</td>\n",
       "      <td>6094808.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TM</th>\n",
       "      <td>116.13</td>\n",
       "      <td>116.83</td>\n",
       "      <td>116.06</td>\n",
       "      <td>116.42</td>\n",
       "      <td>165178.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TSLA</th>\n",
       "      <td>201.35</td>\n",
       "      <td>204.14</td>\n",
       "      <td>199.66</td>\n",
       "      <td>200.95</td>\n",
       "      <td>3316297.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-11</th>\n",
       "      <th>F</th>\n",
       "      <td>12.17</td>\n",
       "      <td>12.17</td>\n",
       "      <td>11.91</td>\n",
       "      <td>11.99</td>\n",
       "      <td>40053504.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Open    High     Low   Close      Volume  is_higher\n",
       "Date       minor                                                       \n",
       "2016-10-10 F       12.35   12.38   12.10   12.12  24951437.0          0\n",
       "           GM      32.31   32.60   32.12   32.15   6094808.0          1\n",
       "           TM     116.13  116.83  116.06  116.42    165178.0          0\n",
       "           TSLA   201.35  204.14  199.66  200.95   3316297.0          1\n",
       "2016-10-11 F       12.17   12.17   11.91   11.99  40053504.0          0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#this dataframe indicates if the stock was higher in 180 days\n",
    "classes = (stock_df.shift(-180) > stock_df).astype(int)\n",
    "\n",
    "X = stock_panel.to_frame()\n",
    "classes = classes.unstack()\n",
    "classes = classes.swaplevel(0, 1).sort_index()\n",
    "classes = classes.to_frame()\n",
    "classes.index.names = ['Date', 'minor']\n",
    "data = X.join(classes).dropna()\n",
    "data.rename(columns={0: 'is_higher'}, inplace=True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>is_higher</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12.35</td>\n",
       "      <td>12.38</td>\n",
       "      <td>12.10</td>\n",
       "      <td>12.12</td>\n",
       "      <td>24951437.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32.31</td>\n",
       "      <td>32.60</td>\n",
       "      <td>32.12</td>\n",
       "      <td>32.15</td>\n",
       "      <td>6094808.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>116.13</td>\n",
       "      <td>116.83</td>\n",
       "      <td>116.06</td>\n",
       "      <td>116.42</td>\n",
       "      <td>165178.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>201.35</td>\n",
       "      <td>204.14</td>\n",
       "      <td>199.66</td>\n",
       "      <td>200.95</td>\n",
       "      <td>3316297.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12.17</td>\n",
       "      <td>12.17</td>\n",
       "      <td>11.91</td>\n",
       "      <td>11.99</td>\n",
       "      <td>40053504.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Open    High     Low   Close      Volume  is_higher\n",
       "0   12.35   12.38   12.10   12.12  24951437.0        0.0\n",
       "1   32.31   32.60   32.12   32.15   6094808.0        1.0\n",
       "2  116.13  116.83  116.06  116.42    165178.0        0.0\n",
       "3  201.35  204.14  199.66  200.95   3316297.0        1.0\n",
       "4   12.17   12.17   11.91   11.99  40053504.0        0.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import patsy\n",
    "X = patsy.dmatrix(\"Open + High + Low + Close + Volume + is_higher - 1\", data.reset_index(),return_type='dataframe')\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\n",
    "lda = LDA()\n",
    "lda.fit(X.iloc[:, :-1], X.iloc[:, -1]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       0.84      0.99      0.91       845\n",
      "        1.0       0.09      0.01      0.01       156\n",
      "\n",
      "avg / total       0.73      0.84      0.77      1001\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print classification_report(X.iloc[:, -1].values,\n",
    "lda.predict(X.iloc[:, :-1]))"
   ]
  }
 ],
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